Overview

Dataset statistics

Number of variables16
Number of observations48895
Missing cells20141
Missing cells (%)2.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.0 MiB
Average record size in memory128.0 B

Variable types

Numeric10
Categorical6

Alerts

name has a high cardinality: 47896 distinct valuesHigh cardinality
host_name has a high cardinality: 11452 distinct valuesHigh cardinality
neighbourhood has a high cardinality: 221 distinct valuesHigh cardinality
last_review has a high cardinality: 1764 distinct valuesHigh cardinality
id is highly overall correlated with host_idHigh correlation
host_id is highly overall correlated with idHigh correlation
latitude is highly overall correlated with neighbourhood_groupHigh correlation
longitude is highly overall correlated with neighbourhood_groupHigh correlation
number_of_reviews is highly overall correlated with reviews_per_monthHigh correlation
reviews_per_month is highly overall correlated with number_of_reviewsHigh correlation
neighbourhood_group is highly overall correlated with latitude and 1 other fieldsHigh correlation
last_review has 10052 (20.6%) missing valuesMissing
reviews_per_month has 10052 (20.6%) missing valuesMissing
minimum_nights is highly skewed (γ1 = 21.82727453)Skewed
name is uniformly distributedUniform
id has unique valuesUnique
number_of_reviews has 10052 (20.6%) zerosZeros
availability_365 has 17533 (35.9%) zerosZeros

Reproduction

Analysis started2023-02-26 11:53:48.446006
Analysis finished2023-02-26 11:54:01.699560
Duration13.25 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

id
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct48895
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19017143
Minimum2539
Maximum36487245
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size382.1 KiB
2023-02-26T17:24:01.849164image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum2539
5-th percentile1222382.7
Q19471945
median19677284
Q329152178
95-th percentile35259101
Maximum36487245
Range36484706
Interquartile range (IQR)19680234

Descriptive statistics

Standard deviation10983108
Coefficient of variation (CV)0.57753724
Kurtosis-1.2277483
Mean19017143
Median Absolute Deviation (MAD)9908242
Skewness-0.090257375
Sum9.2984322 × 1011
Variance1.2062867 × 1014
MonotonicityStrictly increasing
2023-02-26T17:24:01.952695image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2539 1
 
< 0.1%
25583366 1
 
< 0.1%
25551687 1
 
< 0.1%
25552076 1
 
< 0.1%
25554120 1
 
< 0.1%
25568873 1
 
< 0.1%
25571627 1
 
< 0.1%
25572892 1
 
< 0.1%
25580113 1
 
< 0.1%
25580283 1
 
< 0.1%
Other values (48885) 48885
> 99.9%
ValueCountFrequency (%)
2539 1
< 0.1%
2595 1
< 0.1%
3647 1
< 0.1%
3831 1
< 0.1%
5022 1
< 0.1%
5099 1
< 0.1%
5121 1
< 0.1%
5178 1
< 0.1%
5203 1
< 0.1%
5238 1
< 0.1%
ValueCountFrequency (%)
36487245 1
< 0.1%
36485609 1
< 0.1%
36485431 1
< 0.1%
36485057 1
< 0.1%
36484665 1
< 0.1%
36484363 1
< 0.1%
36484087 1
< 0.1%
36483152 1
< 0.1%
36483010 1
< 0.1%
36482809 1
< 0.1%

name
Categorical

HIGH CARDINALITY  UNIFORM 

Distinct47896
Distinct (%)98.0%
Missing16
Missing (%)< 0.1%
Memory size382.1 KiB
Hillside Hotel
 
18
Home away from home
 
17
New york Multi-unit building
 
16
Brooklyn Apartment
 
12
Loft Suite @ The Box House Hotel
 
11
Other values (47891)
48805 

Length

Max length179
Median length78
Mean length36.90321
Min length1

Characters and Unicode

Total characters1803792
Distinct characters776
Distinct categories20 ?
Distinct scripts11 ?
Distinct blocks17 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique47250 ?
Unique (%)96.7%

Sample

1st rowClean & quiet apt home by the park
2nd rowSkylit Midtown Castle
3rd rowTHE VILLAGE OF HARLEM....NEW YORK !
4th rowCozy Entire Floor of Brownstone
5th rowEntire Apt: Spacious Studio/Loft by central park

Common Values

ValueCountFrequency (%)
Hillside Hotel 18
 
< 0.1%
Home away from home 17
 
< 0.1%
New york Multi-unit building 16
 
< 0.1%
Brooklyn Apartment 12
 
< 0.1%
Loft Suite @ The Box House Hotel 11
 
< 0.1%
Private Room 11
 
< 0.1%
#NAME? 10
 
< 0.1%
Private room 10
 
< 0.1%
Artsy Private BR in Fort Greene Cumberland 10
 
< 0.1%
Beautiful Brooklyn Brownstone 8
 
< 0.1%
Other values (47886) 48756
99.7%
(Missing) 16
 
< 0.1%

Length

2023-02-26T17:24:02.069244image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
in 16744
 
5.6%
room 10028
 
3.4%
8413
 
2.8%
bedroom 7601
 
2.5%
private 7151
 
2.4%
apartment 6694
 
2.2%
cozy 4984
 
1.7%
apt 4618
 
1.5%
brooklyn 4049
 
1.4%
studio 3988
 
1.3%
Other values (12552) 224263
75.1%

Most occurring characters

ValueCountFrequency (%)
251335
 
13.9%
e 124601
 
6.9%
o 122293
 
6.8%
t 105238
 
5.8%
a 103554
 
5.7%
r 97923
 
5.4%
i 94635
 
5.2%
n 94598
 
5.2%
l 51721
 
2.9%
m 49112
 
2.7%
Other values (766) 708782
39.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1205955
66.9%
Uppercase Letter 270530
 
15.0%
Space Separator 251339
 
13.9%
Other Punctuation 33845
 
1.9%
Decimal Number 25321
 
1.4%
Dash Punctuation 6859
 
0.4%
Math Symbol 2736
 
0.2%
Other Letter 2547
 
0.1%
Close Punctuation 1537
 
0.1%
Open Punctuation 1395
 
0.1%
Other values (10) 1728
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
82
 
3.2%
46
 
1.8%
44
 
1.7%
41
 
1.6%
38
 
1.5%
37
 
1.5%
36
 
1.4%
36
 
1.4%
30
 
1.2%
29
 
1.1%
Other values (520) 2128
83.5%
Lowercase Letter
ValueCountFrequency (%)
e 124601
 
10.3%
o 122293
 
10.1%
t 105238
 
8.7%
a 103554
 
8.6%
r 97923
 
8.1%
i 94635
 
7.8%
n 94598
 
7.8%
l 51721
 
4.3%
m 49112
 
4.1%
s 48091
 
4.0%
Other values (58) 314189
26.1%
Other Symbol
ValueCountFrequency (%)
266
30.3%
168
19.1%
105
 
11.9%
38
 
4.3%
35
 
4.0%
34
 
3.9%
25
 
2.8%
15
 
1.7%
15
 
1.7%
14
 
1.6%
Other values (50) 164
18.7%
Uppercase Letter
ValueCountFrequency (%)
B 29963
 
11.1%
S 26467
 
9.8%
C 20981
 
7.8%
A 19427
 
7.2%
R 17935
 
6.6%
P 14616
 
5.4%
E 14358
 
5.3%
L 14054
 
5.2%
M 11935
 
4.4%
N 11707
 
4.3%
Other values (33) 89087
32.9%
Other Punctuation
ValueCountFrequency (%)
, 9177
27.1%
! 7855
23.2%
/ 5229
15.4%
. 4375
12.9%
& 3182
 
9.4%
' 1074
 
3.2%
* 1021
 
3.0%
: 597
 
1.8%
# 565
 
1.7%
" 294
 
0.9%
Other values (11) 476
 
1.4%
Math Symbol
ValueCountFrequency (%)
+ 1380
50.4%
| 992
36.3%
~ 271
 
9.9%
= 34
 
1.2%
> 25
 
0.9%
< 20
 
0.7%
6
 
0.2%
4
 
0.1%
2
 
0.1%
× 1
 
< 0.1%
Decimal Number
ValueCountFrequency (%)
1 8661
34.2%
2 6830
27.0%
3 2560
 
10.1%
5 2164
 
8.5%
0 2115
 
8.4%
4 1307
 
5.2%
6 569
 
2.2%
7 450
 
1.8%
8 399
 
1.6%
9 266
 
1.1%
Close Punctuation
ValueCountFrequency (%)
) 1480
96.3%
] 37
 
2.4%
} 9
 
0.6%
8
 
0.5%
3
 
0.2%
Open Punctuation
ValueCountFrequency (%)
( 1339
96.0%
[ 36
 
2.6%
{ 9
 
0.6%
8
 
0.6%
3
 
0.2%
Dash Punctuation
ValueCountFrequency (%)
- 6785
98.9%
47
 
0.7%
26
 
0.4%
1
 
< 0.1%
Modifier Letter
ValueCountFrequency (%)
21
56.8%
11
29.7%
5
 
13.5%
Modifier Symbol
ValueCountFrequency (%)
^ 9
56.2%
` 4
25.0%
´ 3
 
18.8%
Space Separator
ValueCountFrequency (%)
251335
> 99.9%
  4
 
< 0.1%
Final Punctuation
ValueCountFrequency (%)
200
84.0%
38
 
16.0%
Nonspacing Mark
ValueCountFrequency (%)
165
92.2%
14
 
7.8%
Connector Punctuation
ValueCountFrequency (%)
_ 42
97.7%
1
 
2.3%
Initial Punctuation
ValueCountFrequency (%)
40
83.3%
8
 
16.7%
Control
ValueCountFrequency (%)
185
100.0%
Currency Symbol
ValueCountFrequency (%)
$ 94
100.0%
Other Number
ValueCountFrequency (%)
² 9
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1476282
81.8%
Common 324581
 
18.0%
Han 2237
 
0.1%
Cyrillic 191
 
< 0.1%
Inherited 179
 
< 0.1%
Katakana 136
 
< 0.1%
Hiragana 70
 
< 0.1%
Hangul 70
 
< 0.1%
Hebrew 31
 
< 0.1%
Georgian 13
 
< 0.1%

Most frequent character per script

Han
ValueCountFrequency (%)
82
 
3.7%
46
 
2.1%
44
 
2.0%
41
 
1.8%
38
 
1.7%
37
 
1.7%
36
 
1.6%
36
 
1.6%
30
 
1.3%
29
 
1.3%
Other values (401) 1818
81.3%
Common
ValueCountFrequency (%)
251335
77.4%
, 9177
 
2.8%
1 8661
 
2.7%
! 7855
 
2.4%
2 6830
 
2.1%
- 6785
 
2.1%
/ 5229
 
1.6%
. 4375
 
1.3%
& 3182
 
1.0%
3 2560
 
0.8%
Other values (123) 18592
 
5.7%
Latin
ValueCountFrequency (%)
e 124601
 
8.4%
o 122293
 
8.3%
t 105238
 
7.1%
a 103554
 
7.0%
r 97923
 
6.6%
i 94635
 
6.4%
n 94598
 
6.4%
l 51721
 
3.5%
m 49112
 
3.3%
s 48091
 
3.3%
Other values (68) 584516
39.6%
Hangul
ValueCountFrequency (%)
7
 
10.0%
3
 
4.3%
3
 
4.3%
2
 
2.9%
2
 
2.9%
2
 
2.9%
2
 
2.9%
2
 
2.9%
2
 
2.9%
2
 
2.9%
Other values (38) 43
61.4%
Cyrillic
ValueCountFrequency (%)
а 26
13.6%
о 18
 
9.4%
т 17
 
8.9%
н 15
 
7.9%
е 13
 
6.8%
к 11
 
5.8%
р 11
 
5.8%
м 10
 
5.2%
с 9
 
4.7%
в 9
 
4.7%
Other values (23) 52
27.2%
Katakana
ValueCountFrequency (%)
14
 
10.3%
12
 
8.8%
10
 
7.4%
9
 
6.6%
9
 
6.6%
9
 
6.6%
8
 
5.9%
7
 
5.1%
6
 
4.4%
6
 
4.4%
Other values (22) 46
33.8%
Hiragana
ValueCountFrequency (%)
16
22.9%
7
10.0%
7
10.0%
6
 
8.6%
5
 
7.1%
4
 
5.7%
4
 
5.7%
3
 
4.3%
2
 
2.9%
2
 
2.9%
Other values (13) 14
20.0%
Hebrew
ValueCountFrequency (%)
ו 5
16.1%
י 5
16.1%
ר 4
12.9%
ב 4
12.9%
ת 2
 
6.5%
ע 2
 
6.5%
ה 2
 
6.5%
ד 1
 
3.2%
ש 1
 
3.2%
ל 1
 
3.2%
Other values (4) 4
12.9%
Inherited
ValueCountFrequency (%)
165
92.2%
14
 
7.8%
Georgian
ValueCountFrequency (%)
13
100.0%
Devanagari
ValueCountFrequency (%)
2
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1799299
99.8%
CJK 2237
 
0.1%
Misc Symbols 500
 
< 0.1%
None 431
 
< 0.1%
Punctuation 423
 
< 0.1%
Dingbats 320
 
< 0.1%
Cyrillic 191
 
< 0.1%
VS 179
 
< 0.1%
Hiragana 70
 
< 0.1%
Hangul 70
 
< 0.1%
Other values (7) 72
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
251335
 
14.0%
e 124601
 
6.9%
o 122293
 
6.8%
t 105238
 
5.8%
a 103554
 
5.8%
r 97923
 
5.4%
i 94635
 
5.3%
n 94598
 
5.3%
l 51721
 
2.9%
m 49112
 
2.7%
Other values (86) 704289
39.1%
Misc Symbols
ValueCountFrequency (%)
266
53.2%
105
 
21.0%
38
 
7.6%
15
 
3.0%
11
 
2.2%
8
 
1.6%
6
 
1.2%
6
 
1.2%
6
 
1.2%
6
 
1.2%
Other values (12) 33
 
6.6%
Punctuation
ValueCountFrequency (%)
200
47.3%
62
 
14.7%
47
 
11.1%
40
 
9.5%
38
 
9.0%
26
 
6.1%
8
 
1.9%
1
 
0.2%
1
 
0.2%
Dingbats
ValueCountFrequency (%)
168
52.5%
34
 
10.6%
25
 
7.8%
15
 
4.7%
14
 
4.4%
11
 
3.4%
8
 
2.5%
6
 
1.9%
5
 
1.6%
4
 
1.2%
Other values (13) 30
 
9.4%
VS
ValueCountFrequency (%)
165
92.2%
14
 
7.8%
CJK
ValueCountFrequency (%)
82
 
3.7%
46
 
2.1%
44
 
2.0%
41
 
1.8%
38
 
1.7%
37
 
1.7%
36
 
1.6%
36
 
1.6%
30
 
1.3%
29
 
1.3%
Other values (401) 1818
81.3%
None
ValueCountFrequency (%)
35
 
8.1%
à 28
 
6.5%
ó 24
 
5.6%
21
 
4.9%
é 16
 
3.7%
15
 
3.5%
14
 
3.2%
· 13
 
3.0%
12
 
2.8%
11
 
2.6%
Other values (70) 242
56.1%
Cyrillic
ValueCountFrequency (%)
а 26
13.6%
о 18
 
9.4%
т 17
 
8.9%
н 15
 
7.9%
е 13
 
6.8%
к 11
 
5.8%
р 11
 
5.8%
м 10
 
5.2%
с 9
 
4.7%
в 9
 
4.7%
Other values (23) 52
27.2%
Hiragana
ValueCountFrequency (%)
16
22.9%
7
10.0%
7
10.0%
6
 
8.6%
5
 
7.1%
4
 
5.7%
4
 
5.7%
3
 
4.3%
2
 
2.9%
2
 
2.9%
Other values (13) 14
20.0%
Georgian
ValueCountFrequency (%)
13
100.0%
Hangul
ValueCountFrequency (%)
7
 
10.0%
3
 
4.3%
3
 
4.3%
2
 
2.9%
2
 
2.9%
2
 
2.9%
2
 
2.9%
2
 
2.9%
2
 
2.9%
2
 
2.9%
Other values (38) 43
61.4%
Hebrew
ValueCountFrequency (%)
ו 5
16.1%
י 5
16.1%
ר 4
12.9%
ב 4
12.9%
ת 2
 
6.5%
ע 2
 
6.5%
ה 2
 
6.5%
ד 1
 
3.2%
ש 1
 
3.2%
ל 1
 
3.2%
Other values (4) 4
12.9%
Misc Technical
ValueCountFrequency (%)
4
57.1%
1
 
14.3%
1
 
14.3%
1
 
14.3%
Math Operators
ValueCountFrequency (%)
4
57.1%
2
28.6%
1
 
14.3%
Geometric Shapes
ValueCountFrequency (%)
4
36.4%
2
18.2%
2
18.2%
2
18.2%
1
 
9.1%
Devanagari
ValueCountFrequency (%)
2
100.0%
Letterlike Symbols
ValueCountFrequency (%)
1
100.0%

host_id
Real number (ℝ)

Distinct37457
Distinct (%)76.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean67620011
Minimum2438
Maximum2.7432131 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size382.1 KiB
2023-02-26T17:24:02.179775image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum2438
5-th percentile815564.1
Q17822033
median30793816
Q31.0743442 × 108
95-th percentile2.417646 × 108
Maximum2.7432131 × 108
Range2.7431888 × 108
Interquartile range (IQR)99612390

Descriptive statistics

Standard deviation78610967
Coefficient of variation (CV)1.16254
Kurtosis0.16910576
Mean67620011
Median Absolute Deviation (MAD)27543913
Skewness1.2062139
Sum3.3062804 × 1012
Variance6.1796841 × 1015
MonotonicityNot monotonic
2023-02-26T17:24:02.269840image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
219517861 327
 
0.7%
107434423 232
 
0.5%
30283594 121
 
0.2%
137358866 103
 
0.2%
16098958 96
 
0.2%
12243051 96
 
0.2%
61391963 91
 
0.2%
22541573 87
 
0.2%
200380610 65
 
0.1%
7503643 52
 
0.1%
Other values (37447) 47625
97.4%
ValueCountFrequency (%)
2438 1
 
< 0.1%
2571 1
 
< 0.1%
2787 6
< 0.1%
2845 2
 
< 0.1%
2868 1
 
< 0.1%
2881 2
 
< 0.1%
3151 1
 
< 0.1%
3211 1
 
< 0.1%
3415 1
 
< 0.1%
3563 1
 
< 0.1%
ValueCountFrequency (%)
274321313 1
< 0.1%
274311461 1
< 0.1%
274307600 1
< 0.1%
274298453 1
< 0.1%
274273284 1
< 0.1%
274225617 1
< 0.1%
274195458 1
< 0.1%
274188386 1
< 0.1%
274103383 1
< 0.1%
274079964 1
< 0.1%

host_name
Categorical

Distinct11452
Distinct (%)23.4%
Missing21
Missing (%)< 0.1%
Memory size382.1 KiB
Michael
 
417
David
 
403
Sonder (NYC)
 
327
John
 
294
Alex
 
279
Other values (11447)
47154 

Length

Max length35
Median length31
Mean length6.1245857
Min length1

Characters and Unicode

Total characters299333
Distinct characters206
Distinct categories15 ?
Distinct scripts7 ?
Distinct blocks9 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6903 ?
Unique (%)14.1%

Sample

1st rowJohn
2nd rowJennifer
3rd rowElisabeth
4th rowLisaRoxanne
5th rowLaura

Common Values

ValueCountFrequency (%)
Michael 417
 
0.9%
David 403
 
0.8%
Sonder (NYC) 327
 
0.7%
John 294
 
0.6%
Alex 279
 
0.6%
Blueground 232
 
0.5%
Sarah 227
 
0.5%
Daniel 226
 
0.5%
Jessica 205
 
0.4%
Maria 204
 
0.4%
Other values (11442) 46060
94.2%

Length

2023-02-26T17:24:02.378042image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1120
 
2.1%
and 625
 
1.1%
michael 460
 
0.8%
david 449
 
0.8%
sonder 423
 
0.8%
nyc 338
 
0.6%
john 337
 
0.6%
alex 330
 
0.6%
laura 293
 
0.5%
maria 244
 
0.4%
Other values (10259) 49968
91.5%

Most occurring characters

ValueCountFrequency (%)
a 37929
 
12.7%
e 28676
 
9.6%
i 24284
 
8.1%
n 24090
 
8.0%
r 17859
 
6.0%
l 15327
 
5.1%
o 12741
 
4.3%
t 9400
 
3.1%
s 9146
 
3.1%
h 9039
 
3.0%
Other values (196) 110842
37.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 235902
78.8%
Uppercase Letter 54823
 
18.3%
Space Separator 5811
 
1.9%
Other Punctuation 1594
 
0.5%
Open Punctuation 381
 
0.1%
Close Punctuation 379
 
0.1%
Dash Punctuation 208
 
0.1%
Other Letter 110
 
< 0.1%
Decimal Number 83
 
< 0.1%
Math Symbol 34
 
< 0.1%
Other values (5) 8
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
6
 
5.5%
5
 
4.5%
5
 
4.5%
5
 
4.5%
4
 
3.6%
3
 
2.7%
3
 
2.7%
3
 
2.7%
2
 
1.8%
2
 
1.8%
Other values (62) 72
65.5%
Lowercase Letter
ValueCountFrequency (%)
a 37929
16.1%
e 28676
12.2%
i 24284
10.3%
n 24090
10.2%
r 17859
 
7.6%
l 15327
 
6.5%
o 12741
 
5.4%
t 9400
 
4.0%
s 9146
 
3.9%
h 9039
 
3.8%
Other values (54) 47411
20.1%
Uppercase Letter
ValueCountFrequency (%)
A 6459
11.8%
J 5458
 
10.0%
M 5299
 
9.7%
S 4744
 
8.7%
C 3736
 
6.8%
L 2884
 
5.3%
D 2752
 
5.0%
K 2618
 
4.8%
R 2566
 
4.7%
E 2362
 
4.3%
Other values (28) 15945
29.1%
Other Punctuation
ValueCountFrequency (%)
& 1162
72.9%
. 309
 
19.4%
/ 41
 
2.6%
, 35
 
2.2%
' 25
 
1.6%
@ 8
 
0.5%
" 6
 
0.4%
! 4
 
0.3%
: 2
 
0.1%
? 1
 
0.1%
Decimal Number
ValueCountFrequency (%)
5 20
24.1%
7 14
16.9%
0 13
15.7%
2 11
13.3%
4 7
 
8.4%
1 7
 
8.4%
3 4
 
4.8%
6 4
 
4.8%
8 2
 
2.4%
9 1
 
1.2%
Space Separator
ValueCountFrequency (%)
5805
99.9%
6
 
0.1%
Open Punctuation
ValueCountFrequency (%)
( 381
100.0%
Close Punctuation
ValueCountFrequency (%)
) 379
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 208
100.0%
Math Symbol
ValueCountFrequency (%)
+ 34
100.0%
Other Symbol
ValueCountFrequency (%)
2
100.0%
Format
ValueCountFrequency (%)
2
100.0%
Final Punctuation
ValueCountFrequency (%)
2
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 1
100.0%
Currency Symbol
ValueCountFrequency (%)
£ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 290669
97.1%
Common 8498
 
2.8%
Han 91
 
< 0.1%
Cyrillic 56
 
< 0.1%
Hangul 11
 
< 0.1%
Hebrew 5
 
< 0.1%
Hiragana 3
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 37929
 
13.0%
e 28676
 
9.9%
i 24284
 
8.4%
n 24090
 
8.3%
r 17859
 
6.1%
l 15327
 
5.3%
o 12741
 
4.4%
t 9400
 
3.2%
s 9146
 
3.1%
h 9039
 
3.1%
Other values (70) 102178
35.2%
Han
ValueCountFrequency (%)
6
 
6.6%
5
 
5.5%
5
 
5.5%
5
 
5.5%
4
 
4.4%
3
 
3.3%
3
 
3.3%
3
 
3.3%
2
 
2.2%
2
 
2.2%
Other values (45) 53
58.2%
Common
ValueCountFrequency (%)
5805
68.3%
& 1162
 
13.7%
( 381
 
4.5%
) 379
 
4.5%
. 309
 
3.6%
- 208
 
2.4%
/ 41
 
0.5%
, 35
 
0.4%
+ 34
 
0.4%
' 25
 
0.3%
Other values (22) 119
 
1.4%
Cyrillic
ValueCountFrequency (%)
н 6
10.7%
а 6
10.7%
е 6
10.7%
А 4
 
7.1%
и 4
 
7.1%
л 4
 
7.1%
с 3
 
5.4%
к 3
 
5.4%
р 3
 
5.4%
й 3
 
5.4%
Other values (12) 14
25.0%
Hangul
ValueCountFrequency (%)
2
18.2%
2
18.2%
1
9.1%
1
9.1%
1
9.1%
1
9.1%
1
9.1%
1
9.1%
1
9.1%
Hebrew
ValueCountFrequency (%)
ל 1
20.0%
א 1
20.0%
י 1
20.0%
נ 1
20.0%
ד 1
20.0%
Hiragana
ValueCountFrequency (%)
1
33.3%
1
33.3%
1
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 298908
99.9%
None 247
 
0.1%
CJK 91
 
< 0.1%
Cyrillic 56
 
< 0.1%
Hangul 11
 
< 0.1%
Punctuation 10
 
< 0.1%
Hebrew 5
 
< 0.1%
Hiragana 3
 
< 0.1%
Misc Symbols 2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 37929
 
12.7%
e 28676
 
9.6%
i 24284
 
8.1%
n 24090
 
8.1%
r 17859
 
6.0%
l 15327
 
5.1%
o 12741
 
4.3%
t 9400
 
3.1%
s 9146
 
3.1%
h 9039
 
3.0%
Other values (69) 110417
36.9%
None
ValueCountFrequency (%)
é 107
43.3%
í 24
 
9.7%
á 22
 
8.9%
ú 19
 
7.7%
ë 13
 
5.3%
ô 11
 
4.5%
ó 9
 
3.6%
è 7
 
2.8%
ç 5
 
2.0%
ı 4
 
1.6%
Other values (19) 26
 
10.5%
Cyrillic
ValueCountFrequency (%)
н 6
10.7%
а 6
10.7%
е 6
10.7%
А 4
 
7.1%
и 4
 
7.1%
л 4
 
7.1%
с 3
 
5.4%
к 3
 
5.4%
р 3
 
5.4%
й 3
 
5.4%
Other values (12) 14
25.0%
Punctuation
ValueCountFrequency (%)
6
60.0%
2
 
20.0%
2
 
20.0%
CJK
ValueCountFrequency (%)
6
 
6.6%
5
 
5.5%
5
 
5.5%
5
 
5.5%
4
 
4.4%
3
 
3.3%
3
 
3.3%
3
 
3.3%
2
 
2.2%
2
 
2.2%
Other values (45) 53
58.2%
Misc Symbols
ValueCountFrequency (%)
2
100.0%
Hangul
ValueCountFrequency (%)
2
18.2%
2
18.2%
1
9.1%
1
9.1%
1
9.1%
1
9.1%
1
9.1%
1
9.1%
1
9.1%
Hebrew
ValueCountFrequency (%)
ל 1
20.0%
א 1
20.0%
י 1
20.0%
נ 1
20.0%
ד 1
20.0%
Hiragana
ValueCountFrequency (%)
1
33.3%
1
33.3%
1
33.3%
Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size382.1 KiB
Manhattan
21661 
Brooklyn
20104 
Queens
5666 
Bronx
 
1091
Staten Island
 
373

Length

Max length13
Median length9
Mean length8.1824522
Min length5

Characters and Unicode

Total characters400081
Distinct characters20
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBrooklyn
2nd rowManhattan
3rd rowManhattan
4th rowBrooklyn
5th rowManhattan

Common Values

ValueCountFrequency (%)
Manhattan 21661
44.3%
Brooklyn 20104
41.1%
Queens 5666
 
11.6%
Bronx 1091
 
2.2%
Staten Island 373
 
0.8%

Length

2023-02-26T17:24:02.470136image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-26T17:24:02.812235image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
manhattan 21661
44.0%
brooklyn 20104
40.8%
queens 5666
 
11.5%
bronx 1091
 
2.2%
staten 373
 
0.8%
island 373
 
0.8%

Most occurring characters

ValueCountFrequency (%)
n 70929
17.7%
a 65729
16.4%
t 44068
11.0%
o 41299
10.3%
M 21661
 
5.4%
h 21661
 
5.4%
B 21195
 
5.3%
r 21195
 
5.3%
l 20477
 
5.1%
y 20104
 
5.0%
Other values (10) 51763
12.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 350440
87.6%
Uppercase Letter 49268
 
12.3%
Space Separator 373
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 70929
20.2%
a 65729
18.8%
t 44068
12.6%
o 41299
11.8%
h 21661
 
6.2%
r 21195
 
6.0%
l 20477
 
5.8%
y 20104
 
5.7%
k 20104
 
5.7%
e 11705
 
3.3%
Other values (4) 13169
 
3.8%
Uppercase Letter
ValueCountFrequency (%)
M 21661
44.0%
B 21195
43.0%
Q 5666
 
11.5%
S 373
 
0.8%
I 373
 
0.8%
Space Separator
ValueCountFrequency (%)
373
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 399708
99.9%
Common 373
 
0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 70929
17.7%
a 65729
16.4%
t 44068
11.0%
o 41299
10.3%
M 21661
 
5.4%
h 21661
 
5.4%
B 21195
 
5.3%
r 21195
 
5.3%
l 20477
 
5.1%
y 20104
 
5.0%
Other values (9) 51390
12.9%
Common
ValueCountFrequency (%)
373
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 400081
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 70929
17.7%
a 65729
16.4%
t 44068
11.0%
o 41299
10.3%
M 21661
 
5.4%
h 21661
 
5.4%
B 21195
 
5.3%
r 21195
 
5.3%
l 20477
 
5.1%
y 20104
 
5.0%
Other values (10) 51763
12.9%

neighbourhood
Categorical

Distinct221
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size382.1 KiB
Williamsburg
3920 
Bedford-Stuyvesant
3714 
Harlem
 
2658
Bushwick
 
2465
Upper West Side
 
1971
Other values (216)
34167 

Length

Max length26
Median length17
Mean length11.894795
Min length4

Characters and Unicode

Total characters581596
Distinct characters54
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6 ?
Unique (%)< 0.1%

Sample

1st rowKensington
2nd rowMidtown
3rd rowHarlem
4th rowClinton Hill
5th rowEast Harlem

Common Values

ValueCountFrequency (%)
Williamsburg 3920
 
8.0%
Bedford-Stuyvesant 3714
 
7.6%
Harlem 2658
 
5.4%
Bushwick 2465
 
5.0%
Upper West Side 1971
 
4.0%
Hell's Kitchen 1958
 
4.0%
East Village 1853
 
3.8%
Upper East Side 1798
 
3.7%
Crown Heights 1564
 
3.2%
Midtown 1545
 
3.2%
Other values (211) 25449
52.0%

Length

2023-02-26T17:24:02.894256image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
east 6592
 
8.3%
side 4680
 
5.9%
williamsburg 3920
 
5.0%
harlem 3775
 
4.8%
upper 3769
 
4.8%
bedford-stuyvesant 3714
 
4.7%
heights 3586
 
4.5%
village 3164
 
4.0%
west 2759
 
3.5%
bushwick 2465
 
3.1%
Other values (233) 40681
51.4%

Most occurring characters

ValueCountFrequency (%)
e 53470
 
9.2%
i 42282
 
7.3%
s 39625
 
6.8%
t 38587
 
6.6%
a 37608
 
6.5%
l 34448
 
5.9%
r 33667
 
5.8%
30210
 
5.2%
n 26099
 
4.5%
o 24032
 
4.1%
Other values (44) 221568
38.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 461107
79.3%
Uppercase Letter 83934
 
14.4%
Space Separator 30210
 
5.2%
Dash Punctuation 4251
 
0.7%
Other Punctuation 2094
 
0.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 53470
11.6%
i 42282
 
9.2%
s 39625
 
8.6%
t 38587
 
8.4%
a 37608
 
8.2%
l 34448
 
7.5%
r 33667
 
7.3%
n 26099
 
5.7%
o 24032
 
5.2%
d 19663
 
4.3%
Other values (15) 111626
24.2%
Uppercase Letter
ValueCountFrequency (%)
H 11901
14.2%
S 11483
13.7%
B 8374
10.0%
W 8185
9.8%
E 7084
8.4%
C 5327
 
6.3%
U 3833
 
4.6%
G 3723
 
4.4%
F 3281
 
3.9%
V 3209
 
3.8%
Other values (14) 17534
20.9%
Other Punctuation
ValueCountFrequency (%)
' 1968
94.0%
. 124
 
5.9%
, 2
 
0.1%
Space Separator
ValueCountFrequency (%)
30210
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 4251
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 545041
93.7%
Common 36555
 
6.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 53470
 
9.8%
i 42282
 
7.8%
s 39625
 
7.3%
t 38587
 
7.1%
a 37608
 
6.9%
l 34448
 
6.3%
r 33667
 
6.2%
n 26099
 
4.8%
o 24032
 
4.4%
d 19663
 
3.6%
Other values (39) 195560
35.9%
Common
ValueCountFrequency (%)
30210
82.6%
- 4251
 
11.6%
' 1968
 
5.4%
. 124
 
0.3%
, 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 581596
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 53470
 
9.2%
i 42282
 
7.3%
s 39625
 
6.8%
t 38587
 
6.6%
a 37608
 
6.5%
l 34448
 
5.9%
r 33667
 
5.8%
30210
 
5.2%
n 26099
 
4.5%
o 24032
 
4.1%
Other values (44) 221568
38.1%

latitude
Real number (ℝ)

Distinct19048
Distinct (%)39.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40.728949
Minimum40.49979
Maximum40.91306
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size382.1 KiB
2023-02-26T17:24:02.979532image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum40.49979
5-th percentile40.646114
Q140.6901
median40.72307
Q340.763115
95-th percentile40.825643
Maximum40.91306
Range0.41327
Interquartile range (IQR)0.073015

Descriptive statistics

Standard deviation0.054530078
Coefficient of variation (CV)0.0013388531
Kurtosis0.14884466
Mean40.728949
Median Absolute Deviation (MAD)0.03642
Skewness0.23716656
Sum1991442
Variance0.0029735294
MonotonicityNot monotonic
2023-02-26T17:24:03.076830image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40.71813 18
 
< 0.1%
40.68444 13
 
< 0.1%
40.69414 13
 
< 0.1%
40.68634 13
 
< 0.1%
40.76125 12
 
< 0.1%
40.68537 12
 
< 0.1%
40.71171 12
 
< 0.1%
40.71353 12
 
< 0.1%
40.76189 12
 
< 0.1%
40.68683 11
 
< 0.1%
Other values (19038) 48767
99.7%
ValueCountFrequency (%)
40.49979 1
< 0.1%
40.50641 1
< 0.1%
40.50708 1
< 0.1%
40.50868 1
< 0.1%
40.50873 1
< 0.1%
40.50943 1
< 0.1%
40.51133 1
< 0.1%
40.52211 1
< 0.1%
40.52293 1
< 0.1%
40.527 1
< 0.1%
ValueCountFrequency (%)
40.91306 1
< 0.1%
40.91234 1
< 0.1%
40.91169 1
< 0.1%
40.91167 1
< 0.1%
40.90804 1
< 0.1%
40.90734 1
< 0.1%
40.90527 1
< 0.1%
40.90484 1
< 0.1%
40.90406 1
< 0.1%
40.90391 1
< 0.1%

longitude
Real number (ℝ)

Distinct14718
Distinct (%)30.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-73.95217
Minimum-74.24442
Maximum-73.71299
Zeros0
Zeros (%)0.0%
Negative48895
Negative (%)100.0%
Memory size382.1 KiB
2023-02-26T17:24:03.178972image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-74.24442
5-th percentile-74.00388
Q1-73.98307
median-73.95568
Q3-73.936275
95-th percentile-73.865771
Maximum-73.71299
Range0.53143
Interquartile range (IQR)0.046795

Descriptive statistics

Standard deviation0.046156736
Coefficient of variation (CV)-0.0006241431
Kurtosis5.0216461
Mean-73.95217
Median Absolute Deviation (MAD)0.02485
Skewness1.2842102
Sum-3615891.3
Variance0.0021304443
MonotonicityNot monotonic
2023-02-26T17:24:03.487171image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-73.95677 18
 
< 0.1%
-73.95427 18
 
< 0.1%
-73.95405 17
 
< 0.1%
-73.9506 16
 
< 0.1%
-73.94791 16
 
< 0.1%
-73.95332 16
 
< 0.1%
-73.95136 16
 
< 0.1%
-73.95669 15
 
< 0.1%
-73.95742 15
 
< 0.1%
-73.94537 15
 
< 0.1%
Other values (14708) 48733
99.7%
ValueCountFrequency (%)
-74.24442 1
< 0.1%
-74.24285 1
< 0.1%
-74.24084 1
< 0.1%
-74.23986 1
< 0.1%
-74.23914 1
< 0.1%
-74.23803 1
< 0.1%
-74.23059 1
< 0.1%
-74.21238 1
< 0.1%
-74.21017 1
< 0.1%
-74.20941 1
< 0.1%
ValueCountFrequency (%)
-73.71299 1
< 0.1%
-73.7169 1
< 0.1%
-73.71795 1
< 0.1%
-73.71829 1
< 0.1%
-73.71928 1
< 0.1%
-73.72173 1
< 0.1%
-73.72179 1
< 0.1%
-73.72247 1
< 0.1%
-73.72435 1
< 0.1%
-73.72581 1
< 0.1%

room_type
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size382.1 KiB
Entire home/apt
25409 
Private room
22326 
Shared room
 
1160

Length

Max length15
Median length15
Mean length13.535269
Min length11

Characters and Unicode

Total characters661807
Distinct characters17
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPrivate room
2nd rowEntire home/apt
3rd rowPrivate room
4th rowEntire home/apt
5th rowEntire home/apt

Common Values

ValueCountFrequency (%)
Entire home/apt 25409
52.0%
Private room 22326
45.7%
Shared room 1160
 
2.4%

Length

2023-02-26T17:24:03.575701image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-26T17:24:03.656228image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
entire 25409
26.0%
home/apt 25409
26.0%
room 23486
24.0%
private 22326
22.8%
shared 1160
 
1.2%

Most occurring characters

ValueCountFrequency (%)
e 74304
11.2%
t 73144
11.1%
o 72381
10.9%
r 72381
10.9%
a 48895
 
7.4%
48895
 
7.4%
m 48895
 
7.4%
i 47735
 
7.2%
h 26569
 
4.0%
p 25409
 
3.8%
Other values (7) 123199
18.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 538608
81.4%
Space Separator 48895
 
7.4%
Uppercase Letter 48895
 
7.4%
Other Punctuation 25409
 
3.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 74304
13.8%
t 73144
13.6%
o 72381
13.4%
r 72381
13.4%
a 48895
9.1%
m 48895
9.1%
i 47735
8.9%
h 26569
 
4.9%
p 25409
 
4.7%
n 25409
 
4.7%
Other values (2) 23486
 
4.4%
Uppercase Letter
ValueCountFrequency (%)
E 25409
52.0%
P 22326
45.7%
S 1160
 
2.4%
Space Separator
ValueCountFrequency (%)
48895
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 25409
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 587503
88.8%
Common 74304
 
11.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 74304
12.6%
t 73144
12.4%
o 72381
12.3%
r 72381
12.3%
a 48895
8.3%
m 48895
8.3%
i 47735
8.1%
h 26569
 
4.5%
p 25409
 
4.3%
E 25409
 
4.3%
Other values (5) 72381
12.3%
Common
ValueCountFrequency (%)
48895
65.8%
/ 25409
34.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 661807
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 74304
11.2%
t 73144
11.1%
o 72381
10.9%
r 72381
10.9%
a 48895
 
7.4%
48895
 
7.4%
m 48895
 
7.4%
i 47735
 
7.2%
h 26569
 
4.0%
p 25409
 
3.8%
Other values (7) 123199
18.6%

price
Real number (ℝ)

Distinct674
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean152.72069
Minimum0
Maximum10000
Zeros11
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size382.1 KiB
2023-02-26T17:24:03.735752image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile40
Q169
median106
Q3175
95-th percentile355
Maximum10000
Range10000
Interquartile range (IQR)106

Descriptive statistics

Standard deviation240.15417
Coefficient of variation (CV)1.5725058
Kurtosis585.67288
Mean152.72069
Median Absolute Deviation (MAD)46
Skewness19.118939
Sum7467278
Variance57674.025
MonotonicityNot monotonic
2023-02-26T17:24:03.829279image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 2051
 
4.2%
150 2047
 
4.2%
50 1534
 
3.1%
60 1458
 
3.0%
200 1401
 
2.9%
75 1370
 
2.8%
80 1272
 
2.6%
65 1190
 
2.4%
70 1170
 
2.4%
120 1130
 
2.3%
Other values (664) 34272
70.1%
ValueCountFrequency (%)
0 11
 
< 0.1%
10 17
< 0.1%
11 3
 
< 0.1%
12 4
 
< 0.1%
13 1
 
< 0.1%
15 6
 
< 0.1%
16 6
 
< 0.1%
18 2
 
< 0.1%
19 4
 
< 0.1%
20 33
0.1%
ValueCountFrequency (%)
10000 3
< 0.1%
9999 3
< 0.1%
8500 1
 
< 0.1%
8000 1
 
< 0.1%
7703 1
 
< 0.1%
7500 2
< 0.1%
6800 1
 
< 0.1%
6500 3
< 0.1%
6419 1
 
< 0.1%
6000 2
< 0.1%

minimum_nights
Real number (ℝ)

Distinct109
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.0299622
Minimum1
Maximum1250
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size382.1 KiB
2023-02-26T17:24:03.924807image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median3
Q35
95-th percentile30
Maximum1250
Range1249
Interquartile range (IQR)4

Descriptive statistics

Standard deviation20.51055
Coefficient of variation (CV)2.9175903
Kurtosis854.07166
Mean7.0299622
Median Absolute Deviation (MAD)2
Skewness21.827275
Sum343730
Variance420.68264
MonotonicityNot monotonic
2023-02-26T17:24:04.040340image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 12720
26.0%
2 11696
23.9%
3 7999
16.4%
30 3760
 
7.7%
4 3303
 
6.8%
5 3034
 
6.2%
7 2058
 
4.2%
6 752
 
1.5%
14 562
 
1.1%
10 483
 
1.0%
Other values (99) 2528
 
5.2%
ValueCountFrequency (%)
1 12720
26.0%
2 11696
23.9%
3 7999
16.4%
4 3303
 
6.8%
5 3034
 
6.2%
6 752
 
1.5%
7 2058
 
4.2%
8 130
 
0.3%
9 80
 
0.2%
10 483
 
1.0%
ValueCountFrequency (%)
1250 1
 
< 0.1%
1000 1
 
< 0.1%
999 3
 
< 0.1%
500 5
 
< 0.1%
480 1
 
< 0.1%
400 1
 
< 0.1%
370 1
 
< 0.1%
366 1
 
< 0.1%
365 29
0.1%
364 1
 
< 0.1%

number_of_reviews
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct394
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.274466
Minimum0
Maximum629
Zeros10052
Zeros (%)20.6%
Negative0
Negative (%)0.0%
Memory size382.1 KiB
2023-02-26T17:24:04.135870image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median5
Q324
95-th percentile114
Maximum629
Range629
Interquartile range (IQR)23

Descriptive statistics

Standard deviation44.550582
Coefficient of variation (CV)1.9141398
Kurtosis19.529788
Mean23.274466
Median Absolute Deviation (MAD)5
Skewness3.6906346
Sum1138005
Variance1984.7544
MonotonicityNot monotonic
2023-02-26T17:24:04.229403image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 10052
20.6%
1 5244
 
10.7%
2 3465
 
7.1%
3 2520
 
5.2%
4 1994
 
4.1%
5 1618
 
3.3%
6 1357
 
2.8%
7 1179
 
2.4%
8 1127
 
2.3%
9 964
 
2.0%
Other values (384) 19375
39.6%
ValueCountFrequency (%)
0 10052
20.6%
1 5244
10.7%
2 3465
 
7.1%
3 2520
 
5.2%
4 1994
 
4.1%
5 1618
 
3.3%
6 1357
 
2.8%
7 1179
 
2.4%
8 1127
 
2.3%
9 964
 
2.0%
ValueCountFrequency (%)
629 1
< 0.1%
607 1
< 0.1%
597 1
< 0.1%
594 1
< 0.1%
576 1
< 0.1%
543 1
< 0.1%
540 1
< 0.1%
510 1
< 0.1%
488 1
< 0.1%
480 1
< 0.1%

last_review
Categorical

HIGH CARDINALITY  MISSING 

Distinct1764
Distinct (%)4.5%
Missing10052
Missing (%)20.6%
Memory size382.1 KiB
23-06-2019
 
1413
01-07-2019
 
1359
30-06-2019
 
1341
24-06-2019
 
875
07-07-2019
 
718
Other values (1759)
33137 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters388430
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique236 ?
Unique (%)0.6%

Sample

1st row19-10-2018
2nd row21-05-2019
3rd row05-07-2019
4th row19-11-2018
5th row22-06-2019

Common Values

ValueCountFrequency (%)
23-06-2019 1413
 
2.9%
01-07-2019 1359
 
2.8%
30-06-2019 1341
 
2.7%
24-06-2019 875
 
1.8%
07-07-2019 718
 
1.5%
02-07-2019 658
 
1.3%
22-06-2019 655
 
1.3%
16-06-2019 601
 
1.2%
05-07-2019 580
 
1.2%
06-07-2019 565
 
1.2%
Other values (1754) 30078
61.5%
(Missing) 10052
 
20.6%

Length

2023-02-26T17:24:04.331491image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
23-06-2019 1413
 
3.6%
01-07-2019 1359
 
3.5%
30-06-2019 1341
 
3.5%
24-06-2019 875
 
2.3%
07-07-2019 718
 
1.8%
02-07-2019 658
 
1.7%
22-06-2019 655
 
1.7%
16-06-2019 601
 
1.5%
05-07-2019 580
 
1.5%
06-07-2019 565
 
1.5%
Other values (1754) 30078
77.4%

Most occurring characters

ValueCountFrequency (%)
0 92333
23.8%
- 77686
20.0%
1 62027
16.0%
2 58684
15.1%
9 30106
 
7.8%
6 19890
 
5.1%
7 12824
 
3.3%
8 10838
 
2.8%
5 9577
 
2.5%
3 8764
 
2.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 310744
80.0%
Dash Punctuation 77686
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 92333
29.7%
1 62027
20.0%
2 58684
18.9%
9 30106
 
9.7%
6 19890
 
6.4%
7 12824
 
4.1%
8 10838
 
3.5%
5 9577
 
3.1%
3 8764
 
2.8%
4 5701
 
1.8%
Dash Punctuation
ValueCountFrequency (%)
- 77686
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 388430
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 92333
23.8%
- 77686
20.0%
1 62027
16.0%
2 58684
15.1%
9 30106
 
7.8%
6 19890
 
5.1%
7 12824
 
3.3%
8 10838
 
2.8%
5 9577
 
2.5%
3 8764
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 388430
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 92333
23.8%
- 77686
20.0%
1 62027
16.0%
2 58684
15.1%
9 30106
 
7.8%
6 19890
 
5.1%
7 12824
 
3.3%
8 10838
 
2.8%
5 9577
 
2.5%
3 8764
 
2.3%

reviews_per_month
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct937
Distinct (%)2.4%
Missing10052
Missing (%)20.6%
Infinite0
Infinite (%)0.0%
Mean1.3732214
Minimum0.01
Maximum58.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size382.1 KiB
2023-02-26T17:24:04.410732image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile0.04
Q10.19
median0.72
Q32.02
95-th percentile4.64
Maximum58.5
Range58.49
Interquartile range (IQR)1.83

Descriptive statistics

Standard deviation1.680442
Coefficient of variation (CV)1.2237225
Kurtosis42.493469
Mean1.3732214
Median Absolute Deviation (MAD)0.62
Skewness3.1301885
Sum53340.04
Variance2.8238853
MonotonicityNot monotonic
2023-02-26T17:24:04.506807image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.02 919
 
1.9%
1 893
 
1.8%
0.05 893
 
1.8%
0.03 804
 
1.6%
0.16 667
 
1.4%
0.04 655
 
1.3%
0.08 596
 
1.2%
0.09 593
 
1.2%
0.06 579
 
1.2%
0.11 539
 
1.1%
Other values (927) 31705
64.8%
(Missing) 10052
 
20.6%
ValueCountFrequency (%)
0.01 42
 
0.1%
0.02 919
1.9%
0.03 804
1.6%
0.04 655
1.3%
0.05 893
1.8%
0.06 579
1.2%
0.07 466
1.0%
0.08 596
1.2%
0.09 593
1.2%
0.1 457
0.9%
ValueCountFrequency (%)
58.5 1
< 0.1%
27.95 1
< 0.1%
20.94 1
< 0.1%
19.75 1
< 0.1%
17.82 1
< 0.1%
16.81 1
< 0.1%
16.22 1
< 0.1%
16.03 1
< 0.1%
15.78 1
< 0.1%
15.32 1
< 0.1%
Distinct47
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.143982
Minimum1
Maximum327
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size382.1 KiB
2023-02-26T17:24:04.599829image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile15
Maximum327
Range326
Interquartile range (IQR)1

Descriptive statistics

Standard deviation32.952519
Coefficient of variation (CV)4.6126262
Kurtosis67.550888
Mean7.143982
Median Absolute Deviation (MAD)0
Skewness7.9331739
Sum349305
Variance1085.8685
MonotonicityNot monotonic
2023-02-26T17:24:04.687082image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
1 32303
66.1%
2 6658
 
13.6%
3 2853
 
5.8%
4 1440
 
2.9%
5 845
 
1.7%
6 570
 
1.2%
8 416
 
0.9%
7 399
 
0.8%
327 327
 
0.7%
9 234
 
0.5%
Other values (37) 2850
 
5.8%
ValueCountFrequency (%)
1 32303
66.1%
2 6658
 
13.6%
3 2853
 
5.8%
4 1440
 
2.9%
5 845
 
1.7%
6 570
 
1.2%
7 399
 
0.8%
8 416
 
0.9%
9 234
 
0.5%
10 210
 
0.4%
ValueCountFrequency (%)
327 327
0.7%
232 232
0.5%
121 121
 
0.2%
103 103
 
0.2%
96 192
0.4%
91 91
 
0.2%
87 87
 
0.2%
65 65
 
0.1%
52 104
 
0.2%
50 50
 
0.1%

availability_365
Real number (ℝ)

Distinct366
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean112.78133
Minimum0
Maximum365
Zeros17533
Zeros (%)35.9%
Negative0
Negative (%)0.0%
Memory size382.1 KiB
2023-02-26T17:24:04.780130image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median45
Q3227
95-th percentile359
Maximum365
Range365
Interquartile range (IQR)227

Descriptive statistics

Standard deviation131.62229
Coefficient of variation (CV)1.1670575
Kurtosis-0.99753405
Mean112.78133
Median Absolute Deviation (MAD)45
Skewness0.76340758
Sum5514443
Variance17324.427
MonotonicityNot monotonic
2023-02-26T17:24:04.870435image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 17533
35.9%
365 1295
 
2.6%
364 491
 
1.0%
1 408
 
0.8%
89 361
 
0.7%
5 340
 
0.7%
3 306
 
0.6%
179 301
 
0.6%
90 290
 
0.6%
2 270
 
0.6%
Other values (356) 27300
55.8%
ValueCountFrequency (%)
0 17533
35.9%
1 408
 
0.8%
2 270
 
0.6%
3 306
 
0.6%
4 233
 
0.5%
5 340
 
0.7%
6 245
 
0.5%
7 219
 
0.4%
8 233
 
0.5%
9 193
 
0.4%
ValueCountFrequency (%)
365 1295
2.6%
364 491
 
1.0%
363 239
 
0.5%
362 166
 
0.3%
361 111
 
0.2%
360 102
 
0.2%
359 135
 
0.3%
358 180
 
0.4%
357 95
 
0.2%
356 78
 
0.2%

Interactions

2023-02-26T17:23:59.986580image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-26T17:23:50.908183image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-26T17:23:51.931273image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-26T17:23:52.993588image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-26T17:23:53.935835image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-26T17:23:54.918283image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-26T17:23:55.991869image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-26T17:23:56.948154image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-26T17:23:57.899446image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-26T17:23:59.019976image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-26T17:24:00.090618image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-26T17:23:51.045659image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-26T17:23:52.134102image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-26T17:23:53.089176image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-26T17:23:54.033365image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-26T17:23:55.015408image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-26T17:23:56.087397image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-26T17:23:57.044668image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-26T17:23:57.993468image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-26T17:23:59.127273image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-26T17:24:00.186145image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-26T17:23:51.145767image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-26T17:23:52.228267image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-26T17:23:53.180641image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-26T17:23:54.130894image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-26T17:23:55.231058image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-26T17:23:56.179922image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-26T17:23:57.138198image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-26T17:23:58.086997image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-26T17:23:59.220324image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-26T17:24:00.279672image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-26T17:23:51.242298image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-26T17:23:52.324494image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-26T17:23:53.270108image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-26T17:23:54.230425image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-26T17:23:55.322590image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-26T17:23:56.272451image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-26T17:23:57.227695image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-26T17:23:58.186525image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-26T17:23:59.314368image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-26T17:24:00.375203image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-26T17:23:51.341877image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-26T17:23:52.423023image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-26T17:23:53.372657image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-26T17:23:54.335955image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-26T17:23:55.421116image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-26T17:23:56.372982image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-26T17:23:57.327223image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-26T17:23:58.279052image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-26T17:23:59.408897image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-26T17:24:00.469730image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-26T17:23:51.444026image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-26T17:23:52.519551image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-26T17:23:53.461120image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-26T17:23:54.437490image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-26T17:23:55.516646image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-26T17:23:56.468512image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-26T17:23:57.421256image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-26T17:23:58.371581image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-26T17:23:59.502430image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-26T17:24:00.565261image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-26T17:23:51.545559image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-26T17:23:52.613083image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-26T17:23:53.555648image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-26T17:23:54.536021image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-26T17:23:55.612259image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-26T17:23:56.569042image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-26T17:23:57.520393image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-26T17:23:58.468840image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-26T17:23:59.597455image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-26T17:24:00.661788image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-26T17:23:51.640085image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-26T17:23:52.704610image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-26T17:23:53.645173image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-26T17:23:54.631526image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-26T17:23:55.700786image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-26T17:23:56.661569image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-26T17:23:57.613924image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-26T17:23:58.564332image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-26T17:23:59.691987image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-26T17:24:00.759011image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-26T17:23:51.736614image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-26T17:23:52.800037image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-26T17:23:53.747703image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-26T17:23:54.726053image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-26T17:23:55.798313image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-26T17:23:56.756097image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-26T17:23:57.708450image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-26T17:23:58.658862image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-26T17:23:59.786515image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-26T17:24:00.861544image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-26T17:23:51.833143image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-26T17:23:52.894058image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-26T17:23:53.841307image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-26T17:23:54.823169image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-26T17:23:55.895338image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-26T17:23:56.849626image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-26T17:23:57.803917image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-26T17:23:58.917445image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-26T17:23:59.884044image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2023-02-26T17:24:04.952960image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
idhost_idlatitudelongitudepriceminimum_nightsnumber_of_reviewsreviews_per_monthcalculated_host_listings_countavailability_365neighbourhood_grouproom_type
id1.0000.5590.0050.071-0.021-0.058-0.3080.3600.1350.1660.0640.070
host_id0.5591.0000.0500.109-0.072-0.130-0.1280.2680.1470.1730.1000.092
latitude0.0050.0501.0000.0350.1360.022-0.044-0.0230.004-0.0070.5390.117
longitude0.0710.1090.0351.000-0.438-0.1190.0800.1190.0640.0690.6540.158
price-0.021-0.0720.136-0.4381.0000.101-0.055-0.019-0.1060.0860.0180.025
minimum_nights-0.058-0.1300.022-0.1190.1011.000-0.175-0.2890.0640.0760.0030.012
number_of_reviews-0.308-0.128-0.0440.080-0.055-0.1751.0000.7060.0560.2370.0270.021
reviews_per_month0.3600.268-0.0230.119-0.019-0.2890.7061.0000.1460.3920.0480.029
calculated_host_listings_count0.1350.1470.0040.064-0.1060.0640.0560.1461.0000.4070.0890.097
availability_3650.1660.173-0.0070.0690.0860.0760.2370.3920.4071.0000.0830.087
neighbourhood_group0.0640.1000.5390.6540.0180.0030.0270.0480.0890.0831.0000.126
room_type0.0700.0920.1170.1580.0250.0120.0210.0290.0970.0870.1261.000

Missing values

2023-02-26T17:24:01.025123image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-02-26T17:24:01.256774image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-02-26T17:24:01.602537image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

idnamehost_idhost_nameneighbourhood_groupneighbourhoodlatitudelongituderoom_typepriceminimum_nightsnumber_of_reviewslast_reviewreviews_per_monthcalculated_host_listings_countavailability_365
02539Clean & quiet apt home by the park2787JohnBrooklynKensington40.65-73.97Private room1491919-10-20180.216365
12595Skylit Midtown Castle2845JenniferManhattanMidtown40.75-73.98Entire home/apt22514521-05-20190.382355
23647THE VILLAGE OF HARLEM....NEW YORK !4632ElisabethManhattanHarlem40.81-73.94Private room15030NaNNaN1365
33831Cozy Entire Floor of Brownstone4869LisaRoxanneBrooklynClinton Hill40.69-73.96Entire home/apt89127005-07-20194.641194
45022Entire Apt: Spacious Studio/Loft by central park7192LauraManhattanEast Harlem40.80-73.94Entire home/apt8010919-11-20180.1010
55099Large Cozy 1 BR Apartment In Midtown East7322ChrisManhattanMurray Hill40.75-73.97Entire home/apt20037422-06-20190.591129
65121BlissArtsSpace!7356GaronBrooklynBedford-Stuyvesant40.69-73.96Private room60454905-10-20170.4010
75178Large Furnished Room Near B'way8967ShunichiManhattanHell's Kitchen40.76-73.98Private room79243024-06-20193.471220
85203Cozy Clean Guest Room - Family Apt7490MaryEllenManhattanUpper West Side40.80-73.97Private room79211821-07-20170.9910
95238Cute & Cozy Lower East Side 1 bdrm7549BenManhattanChinatown40.71-73.99Entire home/apt150116009-06-20191.334188
idnamehost_idhost_nameneighbourhood_groupneighbourhoodlatitudelongituderoom_typepriceminimum_nightsnumber_of_reviewslast_reviewreviews_per_monthcalculated_host_listings_countavailability_365
4888536482809Stunning Bedroom NYC! Walking to Central Park!!131529729KendallManhattanEast Harlem40.80-73.94Private room7520NaNNaN2353
4888636483010Comfy 1 Bedroom in Midtown East274311461ScottManhattanMidtown40.76-73.97Entire home/apt20060NaNNaN1176
4888736483152Garden Jewel Apartment in Williamsburg New York208514239MelkiBrooklynWilliamsburg40.71-73.94Entire home/apt17010NaNNaN3365
4888836484087Spacious Room w/ Private Rooftop, Central location274321313KatManhattanHell's Kitchen40.76-73.99Private room12540NaNNaN131
4888936484363QUIT PRIVATE HOUSE107716952MichaelQueensJamaica40.69-73.81Private room6510NaNNaN2163
4889036484665Charming one bedroom - newly renovated rowhouse8232441SabrinaBrooklynBedford-Stuyvesant40.68-73.95Private room7020NaNNaN29
4889136485057Affordable room in Bushwick/East Williamsburg6570630MarisolBrooklynBushwick40.70-73.93Private room4040NaNNaN236
4889236485431Sunny Studio at Historical Neighborhood23492952Ilgar & AyselManhattanHarlem40.81-73.95Entire home/apt115100NaNNaN127
488933648560943rd St. Time Square-cozy single bed30985759TazManhattanHell's Kitchen40.76-73.99Shared room5510NaNNaN62
4889436487245Trendy duplex in the very heart of Hell's Kitchen68119814ChristopheManhattanHell's Kitchen40.76-73.99Private room9070NaNNaN123